🔗 Who cites «Intention Analysis Makes LLMs A Good Jailbreak Defender»

17 papers of our library cite it (of the 149,439 whose bibliographies we read) · 5 with a page here, the most cited first · ← back to «Intention Analysis Makes LLMs A Good Jailbreak Defender»

1. Reasoning-to-Defend: Safety-Aware Reasoning Can Defend Large Language Models from JailbreakingConference on Empirical Methods in Natural Language Processing (EMNLP)Junda Zhu, Lingyong Yan, Shuaiqiang Wang, et al.2025 · 21 citations
2. Refuse Whenever You Feel Unsafe: Improving Safety in LLMs via Decoupled Refusal TrainingAnnual Meeting of the Association for Computational Linguistics (ACL)Youliang Yuan, Wenxiang Jiao, Wenxuan Wang, et al.2025 · 19 citations
3. Shaping the Safety Boundaries: Understanding and Defending Against Jailbreaks in Large Language ModelsAnnual Meeting of the Association for Computational Linguistics (ACL)Lang Gao, Jiahui Geng, Xiangliang Zhang, et al.2025 · 19 citations
4. Beyond Surface-Level Detection: Towards Cognitive-Driven Defense Against Jailbreak Attacks via Meta-Operations ReasoningAnnual Meeting of the Association for Computational Linguistics (ACL)Rui Pu, Chaozhuo Li, Rui Ha, et al.2026 · 1 citation
5. LCO: LLM-based Constraint Optimization for Safer Agentic LLMs in Real-world TasksFindings of the Association for Computational LinguisticsJiayong Wan, Jiawei Chen, Zhaoxia Yin, et al.2026

12 more papers of our library cite it; they have no page here yet.

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